{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125634"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125634","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Neural approaches to theorem search & proof repair","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Thomas Reichel, accepted the attached license on 2024-07-12 at 17:16.","The student, Thomas Reichel, submitted this Thesis for approval on 2024-07-12 at 17:25.","This Thesis was approved for publication on 2024-07-16 at 15:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21107 on 2025-02-04 at 21:05:22","This interdisciplinary formal methods/machine learning thesis builds neural automation for two proof-centric tasks that catalyze the reuse of existing proofs: (1) natural language theorem search, in which theorems and their corresponding proofs are retrieved from a database using natural language descriptions and (2) proof repair, in which proofs broken by external changes are mended. The theorem search model is also used as a component of the proof repair tool, allowing it to better interact with the environment. Each task is tackled holistically: we contribute datasets, fine-tuned large language models, and the end-user tools needed to make use of those models."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Neural approaches to theorem search & proof repair"]}]}],"canonical_facts":{"dc:contributor":["Ringer, Talia"],"dc:creator":["Reichel, Thomas"],"dc:date":["2024-07-16","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Thomas Reichel, accepted the attached license on 2024-07-12 at 17:16.","The student, Thomas Reichel, submitted this Thesis for approval on 2024-07-12 at 17:25.","This Thesis was approved for publication on 2024-07-16 at 15:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21107 on 2025-02-04 at 21:05:22","This interdisciplinary formal methods/machine learning thesis builds neural automation for two proof-centric tasks that catalyze the reuse of existing proofs: (1) natural language theorem search, in which theorems and their corresponding proofs are retrieved from a database using natural language descriptions and (2) proof repair, in which proofs broken by external changes are mended. The theorem search model is also used as a component of the proof repair tool, allowing it to better interact with the environment. Each task is tackled holistically: we contribute datasets, fine-tuned large language models, and the end-user tools needed to make use of those models."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125634"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Thomas Reichel"],"dc:subject":["Machine Learning","Formal Methods","Large Language Models","Proof Repair","Theorem Search","Natural Language Search","Coq"],"dc:title":["Neural approaches to theorem search & proof repair"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}